SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Showing 311320 of 982 papers

TitleStatusHype
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hyperbolic Neural NetworksCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47Unverified